Center-within-trial versus trial-level evaluation of surrogate endpoints

Renfro, Lindsay A.; Xue, Yuan; Li, Junlong; Shang, Hongwei; Sargent, Daniel J.

doi:10.1016/j.csda.2014.03.011

Cited by 12 publications

(23 citation statements)

References 29 publications

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“…However, in this example of a single‐clinical trial, the data are grouped according to country, with each country considered to represent a substudy within the trial. Further discussion of this approach can be found in Renfro et al Countries containing 7 or fewer patients were grouped by geographical region to allow for parameter estimation; 2 countries were removed from analysis because of small numbers and the absence of a geographically similar country to combine with (n = 4 and n = 6 patients, respectively). Based on the remaining dataset of 574 patients, results from the application of the surrogacy method show that the

R_{trial}^{2}

point estimate (0.57) likely does not support the use of PFS as a surrogate, whereas the individual‐level surrogacy ( τ = 0.67) could be considered worthy of further investigation.…”

Section: Two‐stage Meta‐analytic Copula Modelmentioning

confidence: 99%

“…However, this study was limited in that the impact of the underlying data‐generation procedure was not considered, only one type of surrogate endpoint with one event of interest was used, and it was based on sample sizes that are not always realistic in practice. Additional studies designed to address some of these concerns have been conducted; however, none have explored the impact on the joint modelling of using a surrogate endpoint that includes the true endpoint as an event of interest.…”

Section: Simulation Studymentioning

confidence: 99%

“…Although small‐sample simulation studies were performed by Burzykowski et al, the authors considered 10 or 20 trials containing 50, 100, or 200 patients, which may be considered too many trials compared to those available within a single‐clinical development plan. Further studies of the two‐stage meta‐analytic copula method have explored small sample sizes; however, these studies did not examine in detail the impact of censoring or changes in the underlying trial and individual‐level surrogacy. Our study therefore considers 4 to 6 clinical trials containing 80 to 120 patients each, estimating both τ and

R_{trial}^{2}

.…”

Section: Simulation Studymentioning

confidence: 99%

“…As can be seen in Table , in addition to factors described above relating to the number and size of trials and type of end point, values of low (0.2), medium (0.5), and high (0.8) individual and trial‐level surrogacy are considered, under varying proportions of censoring. Very few studies have considered low levels of association between endpoints, and those that have were either limited in the number of scenarios under detailed investigation or were based on much larger sample sizes . Additionally, although the range of treatment effects within trials is not of primary interest in this study, previous studies have shown variations in performance of the copula model under various ranges of effects, and so this was added as a final simulation parameter.…”

Section: Simulation Studymentioning

confidence: 99%

“…Although the endpoints here are examples of those in oncology clinical trials, the investigation is applicable to any setting where a potential surrogate endpoint also captures data relevant to the true clinical endpoint. The performance of the method has been assessed previously through simulation studies, including for small sample sizes; however, these studies have focused on the scenario where the surrogate endpoint is defined as the time to one particular event of interest, independent of the true clinical endpoint. The impact of using a surrogate endpoint that is defined as the time to either a short‐term event or the true clinical event of interest will therefore be assessed here.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

An investigation into the two‐stage meta‐analytic copula modelling approach for evaluating time‐to‐event surrogate endpoints which comprise of one or more events of interest

Dimier

Todd

2017

Pharmaceutical Statistics

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Clinical trials of experimental treatments must be designed with primary endpoints that directly measure clinical benefit for patients. In many disease areas, the recognised gold standard primary endpoint can take many years to mature, leading to challenges in the conduct and quality of clinical studies. There is increasing interest in using shorter-term surrogate endpoints as substitutes for costly long-term clinical trial endpoints; such surrogates need to be selected according to biological plausibility, as well as the ability to reliably predict the unobserved treatment effect on the long-term endpoint. A number of statistical methods to evaluate this prediction have been proposed; this paper uses a simulation study to explore one such method in the context of time-to-event surrogates for a time-to-event true endpoint. This two-stage meta-analytic copula method has been extensively studied for time-to-event surrogate endpoints with one event of interest, but thus far has not been explored for the assessment of surrogates which have multiple events of interest, such as those incorporating information directly from the true clinical endpoint. We assess the sensitivity of the method to various factors including strength of association between endpoints, the quantity of data available, and the effect of censoring. In particular, we consider scenarios where there exist very little data on which to assess surrogacy. Results show that the two-stage meta-analytic copula method performs well under certain circumstances and could be considered useful in practice, but demonstrates limitations that may prevent universal use.

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R_{trial}^{2}

point estimate (0.57) likely does not support the use of PFS as a surrogate, whereas the individual‐level surrogacy ( τ = 0.67) could be considered worthy of further investigation.…”

Section: Two‐stage Meta‐analytic Copula Modelmentioning

confidence: 99%

Section: Simulation Studymentioning

confidence: 99%

R_{trial}^{2}

.…”

Section: Simulation Studymentioning

confidence: 99%

Section: Simulation Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An investigation into the two‐stage meta‐analytic copula modelling approach for evaluating time‐to‐event surrogate endpoints which comprise of one or more events of interest

Dimier

Todd

2017

Pharmaceutical Statistics

View full text Add to dashboard Cite

show abstract

Statistical evaluation of surrogate endpoints with examples from cancer clinical trials

et al. 2015

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A surrogate endpoint is intended to replace a clinical endpoint for the evaluation of new treatments when it can be measured more cheaply, more conveniently, more frequently, or earlier than that clinical endpoint. A surrogate endpoint is expected to predict clinical benefit, harm, or lack of these. Besides the biological plausibility of a surrogate, a quantitative assessment of the strength of evidence for surrogacy requires the demonstration of the prognostic value of the surrogate for the clinical outcome, and evidence that treatment effects on the surrogate reliably predict treatment effects on the clinical outcome. We focus on these two conditions, and outline the statistical approaches that have been proposed to assess the extent to which these conditions are fulfilled. When data are available from a single trial, one can assess the "individual level association" between the surrogate and the true endpoint. When data are available from several trials, one can additionally assess the "trial level association" between the treatment effect on the surrogate and the treatment effect on the true endpoint. In the latter case, the "surrogate threshold effect" can be estimated as the minimum effect on the surrogate endpoint that predicts a statistically significant effect on the clinical endpoint. All these concepts are discussed in the context of randomized clinical trials in oncology, and illustrated with two meta-analyses in gastric cancer.

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Individual participant data meta‐analysis of intervention studies with time‐to‐event outcomes: A review of the methodology and an applied example

Jong

Moons

Riley

et al. 2020

Research Synthesis Methods

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Many randomized trials evaluate an intervention effect on time‐to‐event outcomes. Individual participant data (IPD) from such trials can be obtained and combined in a so‐called IPD meta‐analysis (IPD‐MA), to summarize the overall intervention effect. We performed a narrative literature review to provide an overview of methods for conducting an IPD‐MA of randomized intervention studies with a time‐to‐event outcome. We focused on identifying good methodological practice for modeling frailty of trial participants across trials, modeling heterogeneity of intervention effects, choosing appropriate association measures, dealing with (trial differences in) censoring and follow‐up times, and addressing time‐varying intervention effects and effect modification (interactions).We discuss how to achieve this using parametric and semi‐parametric methods, and describe how to implement these in a one‐stage or two‐stage IPD‐MA framework. We recommend exploring heterogeneity of the effect(s) through interaction and non‐linear effects. Random effects should be applied to account for residual heterogeneity of the intervention effect. We provide further recommendations, many of which specific to IPD‐MA of time‐to‐event data from randomized trials examining an intervention effect.We illustrate several key methods in a real IPD‐MA, where IPD of 1225 participants from 5 randomized clinical trials were combined to compare the effects of Carbamazepine and Valproate on the incidence of epileptic seizures.

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Center-within-trial versus trial-level evaluation of surrogate endpoints

Cited by 12 publications

References 29 publications

An investigation into the two‐stage meta‐analytic copula modelling approach for evaluating time‐to‐event surrogate endpoints which comprise of one or more events of interest

An investigation into the two‐stage meta‐analytic copula modelling approach for evaluating time‐to‐event surrogate endpoints which comprise of one or more events of interest

Statistical evaluation of surrogate endpoints with examples from cancer clinical trials

Individual participant data meta‐analysis of intervention studies with time‐to‐event outcomes: A review of the methodology and an applied example

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